刘前进
阜外华中心血管病医院 心血管外科
BACKGROUND:The impact of preoperative statin use on postoperative acute kidney injury (AKI) is uncertain. We aimed to examine the association of statin therapy before cardiac surgery with postoperative AKI.METHODS:The retrospective cohort study consisted of 1581 patients undergoing cardiac surgery. Postoperative AKI were identified by the modified KDIGO definition. Propensity-score matching was employed to control for selection bias, and logistic regression was used to control for confounders. Subgroup and interaction analyses were performed to evaluate the robustness of the findings.RESULTS:The overall incidence of postoperative AKI and severe AKI were 42.19% and 12.27%, respectively. Preoperative moderate-dose statin was significantly associated with a reduced incidence of postoperative AKI (28.9% vs 43.0%, OR (95%CI): 0.54 (0.38, 0.77), p < 0.001) and severe AKI (6.9% vs 13.7%, OR (95%CI): 0.46 (0.26, 0.83), p = 0.009). The beneficial effect on postoperative AKI persisted after adjusting for major confounding factors (OR (95%CI): 0.47 (0.34, 0.66)). Decreased risk of postoperative AKI was observed in patients with preoperative statin duration of 7 ∼ 14 days (OR (95%CI): 0.41 (0.25, 0.65)) and over 14 days (OR (95%CI): 0.43 (0.28, 0.65)), but not in those with preoperative statin duration of <7 days. Similar favorable effects were noted in most subgroup patients, except for those with high-risk factors such as diabetes mellitus, previous congestive cardiac failure, arrhythmia, preoperative ACEI/ARB, aortic cross-clamping or IABP.CONCLUSION:Preoperative moderate-dose statin was significantly related to a decreased risk of postoperative AKI, especially in patients who received statins for a longer duration. Further large-scale multicenter randomized controlled trials are needed to ascertain the impact of statin dose, duration, and timing on postoperative AKI in cardiac surgery patients.
Current medical research and opinion 2024
OBJECTIVE:Postoperative acute kidney injury (PO-AKI) is a common complication after cardiac surgery. We aimed to evaluate whether machine learning algorithms could significantly improve the risk prediction of PO-AKI.METHODS:The retrospective cohort study included 2310 adult patients undergoing cardiac surgery in a tertiary teaching hospital, China. Postoperative AKI and severe AKI were identified by the modified KDIGO definition. The sample was randomly divided into a derivation set and a validation set based on a ratio of 4:1. Exploiting conventional logistic regression (LR) and five ML algorithms including decision tree, random forest, gradient boosting classifier (GBC), Gaussian Naive Bayes and multilayer perceptron, we developed and validated the prediction models of PO-AKI. We implemented the interpretation of models using SHapley Additive exPlanation (SHAP) analysis.RESULTS:Postoperative AKI and severe AKI occurred in 1020 (44.2%) and 286 (12.4%) patients, respectively. Compared with the five ML models, LR model for PO-AKI exhibited the largest AUC (0.812, 95%CI: 0.756, 0.860, all P < 0.05), sensitivity (0.774, 95%CI: 0.719, 0.813), accuracy (0.753, 95%CI: 0.719, 0.781) and Youden index (0.513, 95%CI: 0.451, 0.573). Regarding severe AKI, GBC algorithm showed a significantly higher AUC than the other four ML models (all P < 0.05). Although no significant difference (P = 0.173) was observed in AUCs between GBC (0.86, 95%CI: 0.808, 0.902) and conventional logistic regression (0.803, 95%CI: 0.746, 0.852), GBC achieved greater sensitivity, accuracy and Youden index than conventional LR. Notably, SHAP analyses showed that preoperative serum creatinine, hyperlipidemia, lipid-lowering agents and assisted ventilation time were consistently among the top five important predictors for both postoperative AKI and severe AKI.CONCLUSION:Logistic regression and GBC algorithm demonstrated moderate to good discrimination and superior performance in predicting PO-AKI and severe AKI, respectively. Interpretation of the models identified the key contributors to the predictions, which could potentially inform clinical interventions.
BMC nephrology 2023
BACKGROUND AND OBJECTIVE:The incidence of atrial fibrillation is increasing annually. We develop an automatic detection system, which is of great significance for the early detection and treatment of atrial fibrillation. This can lead to the reduction of the incidence of critical illnesses and mortality.METHODS:We propose an atrial fibrillation detection algorithm based on multi-feature extraction and convolutional neural network of atrial activity via electrocardiograph signals, and compare its detection based on cluster analysis, one-versus-one rule and support vector machine, using accuracy, specificity, sensitivity and true positive rate as evaluation criteria.RESULTS:The atrial fibrillation detection algorithm proposed in this paper has an accuracy rate of 98.92%, a specificity of 97.04%, a sensitivity of 97.19%, and a true positive rate of 96.47%. The average accuracy of the algorithms we compared is 80.26%, and the accuracy of our algorithm is 23.25% higher than this average pertaining to the other algorithms.CONCLUSION:We implemented an atrial fibrillation detection algorithm that meets the requirements of high accuracy, robustness and generalization ability. It has important clinical and social significance for early detection of atrial fibrillation, improvement of patient treatment plans and improvement of medical diagnosis.
Computer methods and programs in biomedicine 2021
